Research Domains

Bayesian Modeling

In order to understand cognition, we want to learn about unobserved cognitive processes. These can be inferred by proposing models and fitting parameters to experimental data. Bayesian statistics not only finds best-fit values for these parameters, but also describes our uncertainty about the inference process. This opens up new avenues for analysing existing models as well as predicting human behavior in a way that is still well interpretable.

Human-Robot-Interaction

Core of our research in the field of Human-Robot Interaction is the human being and human’s perception, attitudes, concerns and emotions towards robots. Our objectiveis to gain a better understanding of how humans will interact with robots and the identification of essential features and characteristics, which can increase the acceptance of robots, reduce prejudices, enhance a social relationship between human and robot and thus support a better collaboration between human and robot.

Individual Differences in Human Reasoning

To this day, a great variety of psychological theories of reasoning coexist aimed at explaining the underlying cognitive mechanism. There is a lack of research on comparing and evaluating these theories under the consideration of moderating and mediating factors in a unified framework. Our research aims to solve this issue, by assessing various, assumed influential factors (e.g., personality traits, reasoning and cognitive abilities, socio-economic status, and development over short time) in human reasoning task.

Neuroscientific Modeling

In order to understand human cognition, we have to consider that it is facilitated by activation in our brains. The aim of neuroscientific modeling is to investigate how the unique mechanisms of neural computation give rise to psychological effects in human reasoning. For that, we use the Neural Engineering Framework to simulate reasoning tasks in neural networks. This enables us to understand which particularities of human thinking can be explained by neural computation.

Predictive Modeling

Cognitive Science and Computer Science have tackled computational modeling from different perspectives. While cognitive science focuses on maximizing the information gained from modeling endeavors, computer science prioritizes performance in applications. In a predictive modeling setting, we aim to draw computer science and cognitive science closer together. We apply techniques from statistical modeling, information and database systems, as well as machine learning to contribute to the interdisciplinary domain of human reasoning.